The course will allow students to learn various methods for the design, implementation, and testing of adaptive systems on the Web, created through Artificial Intelligence techniques, with particular reference to Machine Learning techniques. Specific attention will be paid to Information Retrieval systems, such as search engines, crawlers and document feeds. Classic retrieval models will be studied, such as the Vector Space Model and probabilistic models, document ranking techniques, as well as the PageRank algorithm used by Google. Machine Learning methods in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods necessary for personalized search, and social search applications involving communities of individuals in activities such as content tagging and question answering. The techniques for analyzing social networks (e.g., Facebook and Twitter) will be explored, which will allow us to explore phenomena such as the spread of fake news, the filter bubble, and the polarization of users. Finally, Recommender Systems will be studied, from basic algorithms (e.g., collaborative filtering) to application scenarios (e.g., movies, books, music artists and songs).
teacher profile teaching materials
The teacher will make available the slides from the lectures through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of further texts for passing the exam.
Programme
The course will examine various methods for the design, implementation, and testing of adaptive systems on the Web, realized through Artificial Intelligence techniques. Particular attention will be paid to Information Retrieval systems, such as search engines, and to new and emerging technologies suitable for realizing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space model and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods needed for developing personalized research tools and recommender systems, identifying and analyzing online communities, and social networks (such as Facebook and Twitter).Core Documentation
Lectures will cover topics dealt with in scientific papers and reference texts.The teacher will make available the slides from the lectures through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of further texts for passing the exam.
Type of delivery of the course
TraditionalType of evaluation
Written test, project evaluation.